{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.537558Z",
     "start_time": "2025-02-06T14:04:28.531690Z"
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    "execution": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.145935Z",
     "iopub.status.busy": "2025-02-07T15:04:04.145410Z",
     "iopub.status.idle": "2025-02-07T15:04:04.148188Z",
     "shell.execute_reply": "2025-02-07T15:04:04.147693Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.145913Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.149140Z",
     "iopub.status.busy": "2025-02-07T15:04:04.148745Z",
     "iopub.status.idle": "2025-02-07T15:04:04.156837Z",
     "shell.execute_reply": "2025-02-07T15:04:04.156312Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.149115Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.157862Z",
     "iopub.status.busy": "2025-02-07T15:04:04.157390Z",
     "iopub.status.idle": "2025-02-07T15:04:04.159861Z",
     "shell.execute_reply": "2025-02-07T15:04:04.159437Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.157838Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "iopub.status.busy": "2025-02-07T15:04:04.160405Z",
     "iopub.status.idle": "2025-02-07T15:04:04.187110Z",
     "shell.execute_reply": "2025-02-07T15:04:04.186657Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.160551Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 971229.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.187800Z",
     "iopub.status.busy": "2025-02-07T15:04:04.187636Z",
     "iopub.status.idle": "2025-02-07T15:04:04.196570Z",
     "shell.execute_reply": "2025-02-07T15:04:04.196105Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.187783Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
     "start_time": "2025-02-06T14:04:29.250423Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.198843Z",
     "iopub.status.busy": "2025-02-07T15:04:04.198512Z",
     "iopub.status.idle": "2025-02-07T15:04:04.200953Z",
     "shell.execute_reply": "2025-02-07T15:04:04.200445Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.198823Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.201653Z",
     "iopub.status.busy": "2025-02-07T15:04:04.201488Z",
     "iopub.status.idle": "2025-02-07T15:04:04.207769Z",
     "shell.execute_reply": "2025-02-07T15:04:04.207321Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.201635Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
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   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
   "cell_type": "code",
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   "id": "24e99727ef49605",
   "metadata": {
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "847a70137968848a",
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.382744Z",
     "iopub.status.busy": "2025-02-07T15:04:04.382469Z",
     "iopub.status.idle": "2025-02-07T15:04:04.465847Z",
     "shell.execute_reply": "2025-02-07T15:04:04.465325Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.382714Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.466623Z",
     "iopub.status.busy": "2025-02-07T15:04:04.466445Z",
     "iopub.status.idle": "2025-02-07T15:04:04.472121Z",
     "shell.execute_reply": "2025-02-07T15:04:04.471576Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.466604Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.473120Z",
     "iopub.status.busy": "2025-02-07T15:04:04.472751Z",
     "iopub.status.idle": "2025-02-07T15:04:04.477828Z",
     "shell.execute_reply": "2025-02-07T15:04:04.477273Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.473093Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.4252, 0.5432, 0.0316],\n",
      "        [0.1965, 0.1168, 0.6867]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.478896Z",
     "iopub.status.busy": "2025-02-07T15:04:04.478502Z",
     "iopub.status.idle": "2025-02-07T15:04:04.716842Z",
     "shell.execute_reply": "2025-02-07T15:04:04.716277Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.478852Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.717708Z",
     "iopub.status.busy": "2025-02-07T15:04:04.717506Z",
     "iopub.status.idle": "2025-02-07T15:04:04.953718Z",
     "shell.execute_reply": "2025-02-07T15:04:04.953078Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.717688Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.954931Z",
     "iopub.status.busy": "2025-02-07T15:04:04.954650Z",
     "iopub.status.idle": "2025-02-07T15:04:04.965410Z",
     "shell.execute_reply": "2025-02-07T15:04:04.964895Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.954899Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.966480Z",
     "iopub.status.busy": "2025-02-07T15:04:04.966063Z",
     "iopub.status.idle": "2025-02-07T15:04:04.973255Z",
     "shell.execute_reply": "2025-02-07T15:04:04.972808Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.966450Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.974327Z",
     "iopub.status.busy": "2025-02-07T15:04:04.973891Z",
     "iopub.status.idle": "2025-02-07T15:04:04.980746Z",
     "shell.execute_reply": "2025-02-07T15:04:04.980189Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.974297Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.984978Z",
     "iopub.status.busy": "2025-02-07T15:04:04.984644Z",
     "iopub.status.idle": "2025-02-07T15:04:04.989266Z",
     "shell.execute_reply": "2025-02-07T15:04:04.988771Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.984957Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:04.989983Z",
     "iopub.status.busy": "2025-02-07T15:04:04.989793Z",
     "iopub.status.idle": "2025-02-07T15:04:05.136942Z",
     "shell.execute_reply": "2025-02-07T15:04:05.136433Z",
     "shell.execute_reply.started": "2025-02-07T15:04:04.989964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.137685Z",
     "iopub.status.busy": "2025-02-07T15:04:05.137510Z",
     "iopub.status.idle": "2025-02-07T15:04:05.630365Z",
     "shell.execute_reply": "2025-02-07T15:04:05.629476Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.137666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.631718Z",
     "iopub.status.busy": "2025-02-07T15:04:05.631435Z",
     "iopub.status.idle": "2025-02-07T15:04:05.650953Z",
     "shell.execute_reply": "2025-02-07T15:04:05.650431Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.631688Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.651695Z",
     "iopub.status.busy": "2025-02-07T15:04:05.651514Z",
     "iopub.status.idle": "2025-02-07T15:04:05.656409Z",
     "shell.execute_reply": "2025-02-07T15:04:05.655886Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.651676Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.657143Z",
     "iopub.status.busy": "2025-02-07T15:04:05.656968Z",
     "iopub.status.idle": "2025-02-07T15:04:05.661145Z",
     "shell.execute_reply": "2025-02-07T15:04:05.660619Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.657125Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.661833Z",
     "iopub.status.busy": "2025-02-07T15:04:05.661664Z",
     "iopub.status.idle": "2025-02-07T15:04:05.773735Z",
     "shell.execute_reply": "2025-02-07T15:04:05.773161Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.661815Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.774610Z",
     "iopub.status.busy": "2025-02-07T15:04:05.774291Z",
     "iopub.status.idle": "2025-02-07T15:04:05.778595Z",
     "shell.execute_reply": "2025-02-07T15:04:05.778005Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.774592Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.779591Z",
     "iopub.status.busy": "2025-02-07T15:04:05.779340Z",
     "iopub.status.idle": "2025-02-07T15:04:05.784588Z",
     "shell.execute_reply": "2025-02-07T15:04:05.784050Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.779572Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"share.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.785290Z",
     "iopub.status.busy": "2025-02-07T15:04:05.785108Z",
     "iopub.status.idle": "2025-02-07T15:04:05.789328Z",
     "shell.execute_reply": "2025-02-07T15:04:05.788902Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.785269Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.789956Z",
     "iopub.status.busy": "2025-02-07T15:04:05.789783Z",
     "iopub.status.idle": "2025-02-07T15:04:05.794014Z",
     "shell.execute_reply": "2025-02-07T15:04:05.793584Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.789937Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.794907Z",
     "iopub.status.busy": "2025-02-07T15:04:05.794727Z",
     "iopub.status.idle": "2025-02-07T15:04:05.802768Z",
     "shell.execute_reply": "2025-02-07T15:04:05.802338Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.794888Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.803641Z",
     "iopub.status.busy": "2025-02-07T15:04:05.803418Z",
     "iopub.status.idle": "2025-02-07T15:04:05.976522Z",
     "shell.execute_reply": "2025-02-07T15:04:05.976015Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.803622Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 8, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": True,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:05.977302Z",
     "iopub.status.busy": "2025-02-07T15:04:05.977101Z",
     "iopub.status.idle": "2025-02-07T15:04:06.698525Z",
     "shell.execute_reply": "2025-02-07T15:04:06.697985Z",
     "shell.execute_reply.started": "2025-02-07T15:04:05.977282Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 53374464\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T15:04:06.699527Z",
     "iopub.status.busy": "2025-02-07T15:04:06.699114Z",
     "iopub.status.idle": "2025-02-07T15:30:33.114809Z",
     "shell.execute_reply": "2025-02-07T15:30:33.114288Z",
     "shell.execute_reply.started": "2025-02-07T15:04:06.699506Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "41999it [26:24, 26.50it/s, epoch=39, loss=2.34, val_loss=3.45]                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 40 / global_step 42000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T15:30:33.116377Z",
     "iopub.status.busy": "2025-02-07T15:30:33.115790Z",
     "iopub.status.idle": "2025-02-07T15:30:33.240213Z",
     "shell.execute_reply": "2025-02-07T15:30:33.239636Z",
     "shell.execute_reply.started": "2025-02-07T15:30:33.116351Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/share.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T15:30:33.241341Z",
     "iopub.status.busy": "2025-02-07T15:30:33.240858Z",
     "iopub.status.idle": "2025-02-07T15:30:45.184342Z",
     "shell.execute_reply": "2025-02-07T15:30:45.183811Z",
     "shell.execute_reply.started": "2025-02-07T15:30:33.241320Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "9it [00:00, 87.24it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "18it [00:00, 87.43it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:10, 88.00it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.3168453674138703\n",
      "testing bleu: 0.5946701227856661\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T15:30:45.185369Z",
     "iopub.status.busy": "2025-02-07T15:30:45.184977Z",
     "iopub.status.idle": "2025-02-07T15:30:45.187943Z",
     "shell.execute_reply": "2025-02-07T15:30:45.187426Z",
     "shell.execute_reply.started": "2025-02-07T15:30:45.185339Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T15:30:45.188922Z",
     "iopub.status.busy": "2025-02-07T15:30:45.188720Z",
     "iopub.status.idle": "2025-02-07T15:30:45.719442Z",
     "shell.execute_reply": "2025-02-07T15:30:45.718912Z",
     "shell.execute_reply.started": "2025-02-07T15:30:45.188902Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T15:30:45.720329Z",
     "iopub.status.busy": "2025-02-07T15:30:45.720071Z",
     "iopub.status.idle": "2025-02-07T15:30:46.742242Z",
     "shell.execute_reply": "2025-02-07T15:30:46.741674Z",
     "shell.execute_reply.started": "2025-02-07T15:30:45.720310Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  7%|▋         | 9/128 [00:00<00:01, 88.10it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['man in a white boat on a lake.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
